The fitness landscape metaphor plays a central role in the modeling of optimizing principles in many research fields, ranging from evolutionary biology, where it was first introduced, to management research. Here we consider the ensemble of trajectories of an imitative learning search, in which agents exchange information on their fitness and imitate the fittest agent in the population, with the aim of reaching the global maximum of the fitness landscape. We assess the degree to which the start and end points determine the learning trajectories using two measures, namely the predictability, which yields the probability that two randomly chosen trajectories are the same, and the mean path divergence, which gauges the dissimilarity between two learning trajectories. We find that the predictability is greater in rugged landscapes than in smooth ones. The mean path divergence, however, is strongly affected by the search parameters—population size and imitation propensity—that obliterate the influence of the underlying landscape. The learning trajectories become more deterministic, in the sense that there are fewer distinct trajectories and those trajectories are more similar to each other, with increasing population size and imitation propensity. In addition, we find that the roughness of the learning trajectories, which measures the deviation from additivity of the fitness function, is always greater than the roughness estimated over the entire fitness landscape.